Adaptive Robust Tracking Control Of Uncertain Systems

Adaptive control theory and robust control theory are two effective methods for uncertainsystems. Based on self-tuning controller design method and l₁ optimal robust control, thisdissertation addresses l₁ adaptive robust tracking control. The main work and contribution ofthe thesis are as follows.Firstly, robust adaptive tracking control of uncertain discrete time systems by simultaneousidentification and optimization is considered based on the conflict between robust performanceand separation-principle in self-tuning controller design. The control scheme proposed in thissection is shown to have a good robust stability. Moreover, a computable result of theasymptotically worst-case steady state tracking error is provided, which attains the optimal valuein theory and improves the tracking performance.Secondly, adaptive robust tracking control considering control input in the control objective,i.e. adaptive robust generalized tracking control of uncertain discrete time systems is proposed.The stability of the self-tuning system has been proved, and a computable tight upper bound ongeneral asymptotic robust steady state tracking performance is provided. In control engineering,the control input always has an upper bound. The controller design method has considered themagnitude of control input in the performance functions in its design process. So the proposedmethod can be used more widely.